Summary of Continuous Control Reinforcement Learning: Distributed Distributional Drq Algorithms, by Zehao Zhou
Continuous Control Reinforcement Learning: Distributed Distributional DrQ Algorithms
by Zehao Zhou
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Distributed Distributional DrQ algorithm is a model-free and off-policy reinforcement learning (RL) method for continuous control tasks. It’s an actor-critic approach that uses data augmentation and a distributional perspective on critic values. The goal is to learn control policies in high-dimensional continuous spaces. Building on the success of DrQ-v2, which used DDPG as its backbone, this paper modifies the algorithm by using Distributed Distributional DDPG as its backbone, aiming to improve performance in challenging continuous control tasks through better expression of distributional value functions and distributed actor policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Distributed Distributional DrQ is a new way for robots and computers to learn how to do things without being told exactly how. It’s like learning a new skill by watching someone else do it, rather than following instructions step-by-step. This method works really well in situations where the robot or computer has to make decisions based on what it sees and experiences. The goal is to make robots and computers better at doing things that require coordination and control. |
Keywords
» Artificial intelligence » Data augmentation » Reinforcement learning
